Aims: Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings. Background: Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance. Objective: This analytical review aims at systematization of bioinformatics and experimental identification of hub-genes, associated with CRC, for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection. Results: This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection. Conclusion: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Epigenetic therapy is a promising tool for the treatment of a wide range of diseases. Several fundamental epigenetic approaches have been proposed. Firstly, the use of small molecules as epigenetic effectors, as the most developed pharmacological method, has contributed to the introduction of a number of drugs into clinical practice. Secondly, various innovative epigenetic approaches based on dCas9 and the use of small non-coding RNAs as therapeutic agents are also under extensive research. In this review, we present the current state of research in the field of epigenetic therapy, considering the prospects for its application and possible limitations.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide and the second leading cause of death among all cancer types. Deregulation of the networks of tissue-specific transcription factors (TFs) observed in HCC leads to profound changes in the hepatic transcriptional program that facilitates tumor progression. In addition, recent reports suggest that substantial aberrations in the production of TF isoforms occur in HCC. In vitro experiments have identified distinct isoform-specific regulatory functions and related biological effects of liver-specific TFs that are implicated in carcinogenesis, which may be relevant for tumor progression and clinical outcome. This study reviews available data on the expression of isoforms of liver-specific and ubiquitous TFs in the liver and HCC and their effects, including HNF4α, C/EBPs, p73 and TCF7L2, and indicates that assessment of the ratio of isoforms and targeting specific TF variants may be beneficial for the prognosis and treatment of HCC.

Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3–0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.

Background: HML-2 subgroup mobile genetic elements of the HERV-K family were described to participate in carcinogenesis processes, but their expression and epigenetic regulation in molecular subtypes of colorectal cancer (CRC) remain partly characterized. The present study aimed to evaluate the expression of HML-2 elements using RNA-sequencing data in paired tumor and normal intestinal tissue samples from 63 patients with CRC to identify patterns of the retrotransposons’ activity in different molecular subtypes (CMSs). Methods: RNA-sequencing and DNA methylation data were analyzed for paired CRC and normal tissue samples. HERV-K expression was assessed using three bioinformatics tools: Telescope (version 1.0.3), TEtranscripts (version 2.2.3), GeneTEFlow (version 2020). Molecular tumor subtypes were defined using the CMScaller (version 0.99.2) program. The results of the HML-2 loci expression analysis were supplemented with the HML-2 proteins expression data obtained by quantitative RT-PCR. Results: HML-2 expression assessment by GeneTEFlow (version 2020), TECount (version 2.2.3) and Telescope (version 1.0.3) showed high convergence: the Pearson correlation coefficient for each tool exceeded 0.88. Several HML-2 loci were identified as differentially expressed in CRC samples of different CMS. The PCR results confirmed an increase in HML-2 expression in tumor tissues. For all CMSs, an inverse association was detected between differential methylation of CpG sites and differential expression of HML-2 loci. Associations of HML-2 expressions with differentially expressed genes in which they are located were found, and for a number of such genes an inverse relationship between the expression level and the methylation level of their promoters were demonstrated, and data on the involvement in the pathogenesis of CRC were described: CR1, CD48, TTLL3, ABCC2 and ZNF420. Expression signatures associated with the activity of the RIG-I-like receptor signaling cascade were identified in CMS1–3 CRC samples, which may indicate the possible implementation of viral mimicry against the background of HML-2 activation. Conclusions: Analysis of the expression of HML-2 and its association with CpG methylation contributes to a comprehensive interpretation of the CRC pathogenesis mechanisms.